Radar Altimetry as a Proxy for Determining Terrestrial Water Storage Variability in Tropical Basins

The Gravity Recovery and Climate Experiment (GRACE) mission has provided us with unforeseen information on terrestrial water-storage (TWS) variability, contributing to our understanding of global hydrological processes, including hydrological extreme events and anthropogenic impacts on water storage. Attempts to decompose GRACE-based TWS signals into its different water storage layers, i.e., surface water storage (SWS), soil moisture, groundwater and snow, have shown that SWS is a principal component, particularly in the tropics, where major rivers flow over arid regions at high latitudes. Here, we demonstrate that water levels, measured with radar altimeters at a limited number of locations, can be used to reconstruct gridded GRACE-based TWS signals in the Amazon basin, at spatial resolutions ranging from 0.5 to 3, with mean absolute errors (MAE) as low as 2.5 cm and correlations as high as 0.98. We show that, at 3 spatial resolution, spatially-distributed TWS time series can be precisely reconstructed with as few as 41 water-level time series located within the basin. The proposed approach is competitive when compared to existing TWS estimates derived from physically based and computationally expensive methods. Also, a validation experiment indicates that TWS estimates can be extrapolated to periods beyond that of the model regression with low errors. The approach is robust, based on regression models and interpolation techniques, and offers a new possibility to reproduce spatially and temporally distributed TWS that could be used to fill inter-mission gaps and to extend GRACE-based TWS time series beyond its timespan.

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Time Series Analysis of Long-term Terrestrial Water Storage over Canada from GRACE Satellites Using Principal Component Analysis

Canadian Journal of Remote Sensing ◽

10.1080/07038992.2016.1166042 ◽

2016 ◽

Vol 42 (3) ◽

pp. 161-170 ◽

Cited By ~ 9

Author(s):

Junhua Li ◽

Shusen Wang ◽

Fuqun Zhou

Keyword(s):

Principal Component Analysis ◽

Time Series ◽

Time Series Analysis ◽

Water Storage ◽

Principal Component ◽

Component Analysis ◽

Terrestrial Water Storage ◽

Terrestrial Water ◽

Grace Satellites

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Changes in terrestrial water storage versus rainfall and discharges in the Amazon basin

International Journal of Climatology ◽

10.1002/joc.3647 ◽

2013 ◽

Vol 33 (14) ◽

pp. 3029-3046 ◽

Cited By ~ 56

Author(s):

Frédéric Frappart ◽

Guillaume Ramillien ◽

Josyane Ronchail

Keyword(s):

Amazon Basin ◽

Water Storage ◽

Terrestrial Water Storage ◽

Terrestrial Water

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Past terrestrial water storage (1980–2008) in the Amazon Basin reconstructed from GRACE and in situ river gauging data

Hydrology and Earth System Sciences ◽

10.5194/hess-15-533-2011 ◽

2011 ◽

Vol 15 (2) ◽

pp. 533-546 ◽

Cited By ~ 35

Author(s):

M. Becker ◽

B. Meyssignac ◽

L. Xavier ◽

A. Cazenave ◽

R. Alkama ◽

...

Keyword(s):

Large Scale ◽

Amazon Basin ◽

Hydrological Modeling ◽

Water Cycle ◽

Southern Oscillation ◽

Water Storage ◽

Terrestrial Water Storage ◽

Direct Estimate ◽

Terrestrial Water

Abstract. Terrestrial water storage (TWS) composed of surface waters, soil moisture, groundwater and snow where appropriate, is a key element of global and continental water cycle. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) space gravimetry mission provides a new tool to measure large-scale TWS variations. However, for the past few decades, direct estimate of TWS variability is accessible from hydrological modeling only. Here we propose a novel approach that combines GRACE-based TWS spatial patterns with multi-decadal-long in situ river level records, to reconstruct past 2-D TWS over a river basin. Results are presented for the Amazon Basin for the period 1980–2008, focusing on the interannual time scale. Results are compared with past TWS estimated by the global hydrological model ISBA-TRIP. Correlations between reconstructed past interannual TWS variability and known climate forcing modes over the region (e.g., El Niño-Southern Oscillation and Pacific Decadal Oscillation) are also estimated. This method offers new perspective for improving our knowledge of past interannual TWS in world river basins where natural climate variability (as opposed to direct anthropogenic forcing) drives TWS variations.

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Data-driven and physically informed modelling of the Terrestrial Water Storage dynamics

10.5194/egusphere-egu21-15253 ◽

2021 ◽

Author(s):

Karim Douch ◽

Peyman Saemian ◽

Nico Sneeuw

Keyword(s):

Transition Matrix ◽

Amazon Basin ◽

Stochastic Matrix ◽

Water Storage ◽

Water Volume ◽

Terrestrial Water Storage ◽

Total Water ◽

Monthly Basis ◽

Terrestrial Water ◽

Volume Anomaly

The Gravity Recovery and Climate Experiment (GRACE) mission, and its successor GRACE Follow-On, have enabled to map on a monthly basis the Terrestrial Water Storage Anomaly (TWSA) since 2002. This unprecedented capability has provided hydrologists with new observations of the spatiotemporal evolution of TWSA, which have been used, among others, to better constrain numerical runoff models, to characterize empirically the relations between runoff and TWSA, or to simply monitor and quantify groundwater depletions. In this study, we explore the possibility to infer from GRACE observations a physically informed and linear dynamical system that models the intrinsic dynamics of TWSA at sub-basin scales.First, we apply a hexagonal binning over the study area and aggregate the total water volume anomaly derived from GRACE data for each bin. Assuming that each bin exchanges water with the others in proportion to its water content, we then reformulate the mass balance equation of the whole basin as a first order matrix differential equation. All the proportionality coefficients encoding the bin exchanges are gathered in an unknown transition matrix to be determined. &#160;Such a transition matrix must satisfy different algebraic properties to be physically consistent and interpretable. In particular, we show that this matrix is necessarily a left stochastic matrix. Finally, we used the time series of total water volume anomaly to estimate this transition matrix by solving an optimization problem on the manifold defined by the aforementioned matrix constraint. This method is applied to the Amazon basin and to mainland Australia respectively, and the predictive performances of the derived dynamical systems are quantified and discussed.

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Reconstruction of continuous GRACE/GRACE-FO terrestrial water storage anomalies based on time series decomposition

Journal of Hydrology ◽

10.1016/j.jhydrol.2021.127018 ◽

2021 ◽

pp. 127018

Author(s):

Xinchun Yang ◽

Siyuan Tian ◽

Wei You ◽

Zhongshan Jiang

Keyword(s):

Time Series ◽

Water Storage ◽

Terrestrial Water Storage ◽

Time Series Decomposition ◽

Terrestrial Water

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Extracting terrestrial water storage signals from GRACE solutions in the Amazon Basin using an iterative filtering approach

Remote Sensing Letters ◽

10.1080/2150704x.2021.1981557 ◽

2021 ◽

Vol 13 (1) ◽

pp. 14-23

Author(s):

Lun Pu ◽

Dongming Fan ◽

Wei You ◽

Xinchun Yang ◽

Zemede M. Nigatu ◽

...

Keyword(s):

Amazon Basin ◽

Water Storage ◽

Terrestrial Water Storage ◽

Terrestrial Water ◽

Iterative Filtering ◽

Filtering Approach

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PCA study of the interannual variability of GPS heights and environmental parameters

10.5194/egusphere-egu2020-2616 ◽

2020 ◽

Author(s):

Letizia Elia ◽

Susanna Zerbini ◽

Fabio Raicich

Keyword(s):

Time Series ◽

Interannual Variability ◽

Water Storage ◽

Environmental Parameters ◽

Space Time ◽

Terrestrial Water Storage ◽

Coupled Modes ◽

Entire Study ◽

Terrestrial Water ◽

The Mediterranean

Time series of GPS coordinates longer than two decades are now available at many stations around the world. The objective of our study is to investigate large networks of GPS stations to identify and analyze spatially coherent signals present in the coordinate time series and, at the same locations, to identify and analyze common patterns in the series of environmental parameters and climate indexes. The study is confined to Europe and the Mediterranean area, where 107 GPS stations were selected from the Nevada Geodetic Laboratory (NGL) archive on the basis of the completeness and length of the data series. The parameters of interest for this study are the stations height (H), the atmospheric surface pressure (AP), the terrestrial water storage (TWS) and the various climate indexes, such as NAO (North Atlantic Oscillation), AO (Artic Oscillation), SCAND (Scandinavian Index) and MEI (Multivariate ENSO Index). The Empirical Orthogonal Function (EOF) is the methodology adopted to extract the main patterns of space/time variability of these parameters. We also focus on the coupled modes of space/time interannual variability between pairs of variables using the singular value decomposition (SVD) methodology. The coupled variability between all the afore mentioned parameters is investigated. It shall be pointed out that EOF and SVD are mathematical tools providing common modes on the one hand, and statistical correlations between pairs of parameters on the other. Therefore, these methodologies do not allow to directly infer the physical mechanisms responsible for the observed behaviors which should be explained through appropriate modelling. Our study has identified, over Europe and the Mediterranean, main modes of variability in the time series of GPS heights, atmospheric pressure and terrestrial water storage. For example, regarding the station heights, the EOF1 explains about 30% of the variance and the spatial pattern is coherent over the entire study area. The SVD analysis of coupled parameters, namely H-AP, TWS-AP and H-TWS, showed that most of the common variability is explained by the first 3 modes. In particular, 70% for the H-AP, 67% for the TWS-AP and 49% for the H-TWS pair. Moreover, we correlated the stations heights with the NAO, AO, SCAND and MEI indexes to investigate the possible influence of climate variability on the height behavior. To do so, the stations heights were represented using the first three EOFs to reduce the potential effect of local anomalies. More than 30 stations, over the total of 107, show significant correlations up to about 0.3 with the AO and SCAND indexes. The correlation coefficients with MEI turn out to be significant and up to 0.5 for about half of the stations.

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Continental-Scale Basin Water Storage Variation from Global and Dynamically Downscaled Atmospheric Water Budgets in Comparison with GRACE-Derived Observations

Journal of Hydrometeorology ◽

10.1175/jhm-d-11-0143.1 ◽

2012 ◽

Vol 13 (5) ◽

pp. 1589-1603 ◽

Cited By ~ 13

Author(s):

Benjamin Fersch ◽

Harald Kunstmann ◽

András Bárdossy ◽

Balaji Devaraju ◽

Nico Sneeuw

Keyword(s):

Large Scale ◽

Amazon Basin ◽

Climate Model ◽

Water Storage ◽

Terrestrial Water Storage ◽

Atmospheric Water ◽

Flux Divergence ◽

Water Budgets ◽

Continental Scale ◽

Terrestrial Water

Abstract Since 2002, the Gravity Recovery and Climate Experiment (GRACE) has provided gravity-derived observations of variations in the terrestrial water storage. Because of the lack of suitable direct observations of large-scale water storage changes, a validation of the GRACE observations remains difficult. An approach that allows the evaluation of terrestrial water storage variations from GRACE by a comparison with those derived from aerologic water budgets using the atmospheric moisture flux divergence is presented. In addition to reanalysis products from the European Centre for Medium-Range Weather Forecasts and the National Centers for Environmental Prediction, high-resolution regional atmospheric simulations were produced with the Weather Research and Forecast modeling system (WRF) and validated against globally gridded observational data of precipitation and 2-m temperature. The study encompasses six different climatic and hydrographic regions: the Amazon basin, the catchments of Lena and Yenisei, central Australia, the Sahara, the Chad depression, and the Niger. Atmospheric-related uncertainty bounds based on the range of the ensemble of estimated terrestrial water storage variations were computed using different configurations of the regional climate model WRF and different global reanalyses. Atmospheric-related uncertainty ranges with those originating from the GRACE products of GeoForschungsZentrum Potsdam, the Center for Space Research, and the Jet Propulsion Laboratory were also compared. It is shown that dynamically downscaled atmospheric fields are able to add value to global reanalyses, depending on the geographical location of the considered catchments. Global and downscaled atmospheric water budgets are in reasonable agreement (r ≈ 0.7 − 0.9) with GRACE-derived terrestrial mass variations. However, atmospheric- and satellite-based approaches show shortcomings for regions with small storage change rates (<20–25 mm month−1).

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Random Forest-Based Reconstruction and Application of the GRACE Terrestrial Water Storage Estimates for the Lancang-Mekong River Basin

Remote Sensing ◽

10.3390/rs13234831 ◽

2021 ◽

Vol 13 (23) ◽

pp. 4831

Author(s):

Senlin Tang ◽

Hong Wang ◽

Yao Feng ◽

Qinghua Liu ◽

Tingting Wang ◽

...

Keyword(s):

Time Series ◽

Random Forest ◽

Correlation Coefficient ◽

River Basin ◽

Water Storage ◽

Mekong River ◽

Climatic Research Unit ◽

Terrestrial Water Storage ◽

Mekong River Basin ◽

Terrestrial Water

Terrestrial water storage (TWS) is a critical variable in the global hydrological cycle. The TWS estimates derived from the Gravity Recovery and Climate Experiment (GRACE) allow us to better understand water exchanges between the atmosphere, land surface, sea, and glaciers. However, missing historical (pre-2002) GRACE data limit their further application. In this study, we developed a random forest (RF) model to reconstruct the monthly terrestrial water storage anomaly (TWSA) time series using Global Land Data Assimilation System (GLDAS) and Climatic Research Unit (CRU) data for the Lancang-Mekong River basin. The results show that the RF-built TWSA time series agrees well with the GRACE TWSA time series for 2003–2014, showing that correlation coefficients (R) of 0.97 and 0.90 at the basin and grid scales, respectively, which demonstrates the reliability of the RF model. Furthermore, this method is used to reconstruct the historical TWSA time series for 1980–2002. Moreover, the discharge can be obtained by subtracting the evapotranspiration (ET) and RF-built terrestrial water storage change (TWSC) from the precipitation. The comparison between the discharge calculated from the water balance method and the observed discharge showed significant consistency, with a correlation coefficient of 0.89 for 2003–2014 but a slightly lower correlation coefficient (0.86) for 1980–2002. The methods and findings in this study can provide an effective means of reconstructing the TWSA and discharge time series in basins with sparse hydrological data.

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